How to Run Claude Code, Gemini CLI, and Codex Side by Side (And Why You Should)

You are probably already using at least one AI coding agent. Maybe you have Claude Code running in a terminal right now. But if you are only running one agent at a time, you are leaving serious throughput on the table.

The developers shipping the fastest in 2026 are not picking one agent and sticking with it. They are running Claude Code, Gemini CLI, and Codex simultaneously, each assigned to the task it handles best. Think of it less like choosing a favorite tool and more like staffing a team where each member has a different specialty.

This guide covers the practical how: which agent excels at what, how to split work between them without creating merge conflicts, and a real workflow for building a feature with three agents running in parallel.

Why Single-Agent Workflows Hit a Ceiling

Every AI coding agent has a latency floor. Even with the fastest models, a complex architectural change takes 30-90 seconds of agent thinking time. A test suite generation might take two minutes. Research across a large codebase can take even longer.

When you run a single agent, you are serializing work that could be parallel. You ask Claude to refactor a module, wait for it to finish, then ask it to write tests, wait again, then ask it to update the docs. Three sequential tasks that could have been three simultaneous tasks.

The math is straightforward. If each task takes 2 minutes of agent time:

• Single agent, sequential: 6 minutes wall clock time
• Three agents, parallel: 2 minutes wall clock time

That is a 3x speedup on every feature. Over a full day of development, the compounding effect is significant.

But raw parallelism is not the only reason. Each agent has different strengths, and assigning the right task to the right agent produces better output than forcing one model to do everything.

What Each Agent Does Best

After months of running these three agents side by side, clear patterns emerge in where each one excels. This is not about which agent is "better" overall -- it is about which one you want handling a specific category of work.

Claude Code: Architecture and Refactoring

Claude Code's strength is deep reasoning across interconnected files. When you need to refactor a module that touches six other files, Claude consistently produces changes that account for downstream effects. It is also the strongest at understanding existing architectural patterns and extending them consistently.

Best tasks for Claude Code:

• Large refactors that span multiple files
• Designing new system architecture from requirements
• Complex bug investigation (reading stack traces, tracing data flow)
• Code review and identifying subtle issues
• Writing migration scripts that need to handle edge cases

Gemini CLI: Research and Large Context

Gemini's massive context window is its defining advantage. When you need an agent to ingest an entire codebase, a lengthy API specification, or multiple documentation pages before producing output, Gemini handles it without the truncation issues that plague smaller context windows.

Best tasks for Gemini CLI:

• Researching unfamiliar libraries or APIs before implementation
• Analyzing large codebases to produce architectural summaries
• Cross-referencing documentation with existing code
• Generating comprehensive documentation from source
• Understanding and working within monorepos

Codex: Tests, Scaffolding, and Repetitive Generation

Codex excels at structured, pattern-based generation. Give it a clear specification and a pattern to follow, and it will produce consistent, well-structured output quickly. It is particularly strong at test generation where the structure is predictable but the coverage needs to be thorough.

Best tasks for Codex:

• Generating test suites from existing implementations
• Scaffolding boilerplate (CRUD endpoints, model definitions, form components)
• Converting between formats (REST to GraphQL, JSON to TypeScript types)
• Batch file generation following an established pattern
• Writing repetitive but necessary glue code

The Multi-Agent Workflow: Assigning Focused Tasks

Running three agents simultaneously is not just about opening three terminals. You need a system for dividing work so agents do not step on each other. Here is the framework that works.

Rule 1: Separate by File Scope

The simplest way to avoid conflicts is to assign each agent to different files or directories. If Claude is refactoring src/api/, Codex should not be touching those same files. Give Codex the test files in tests/api/ instead.

Rule 2: Use a Task Queue Pattern

Before starting, break your feature into discrete tasks and assign them upfront:

# Task assignments for "Add user notification preferences" feature

# Claude Code (architecture + core logic)
- Design the NotificationPreference data model
- Implement the preference service with conflict resolution
- Wire up the API routes with proper validation

# Gemini CLI (research + documentation)
- Research the push notification provider API docs
- Analyze existing notification code for patterns to follow
- Write the API documentation for the new endpoints

# Codex (tests + scaffolding)
- Generate the database migration files
- Write unit tests for the preference service
- Scaffold the React settings panel component

Rule 3: Sync Points

Not everything can run in parallel. Identify dependencies between tasks and create sync points. For example, Codex cannot write tests for a service Claude has not finished yet. The workflow looks like:

1. Phase 1 (parallel): Claude designs the data model, Gemini researches the notification API, Codex scaffolds the migration
2. Sync: Review Claude's data model, commit it
3. Phase 2 (parallel): Claude implements the service, Gemini writes docs based on the model, Codex generates tests against the interfaces
4. Sync: Run tests, review all output, merge

Practical Example: Building a Feature With Three Agents

Let's walk through a concrete scenario. You are adding a real-time notification preferences system to a Node.js/React application. Here is exactly how this plays out with three agents.

Phase 1: Foundation (All Three in Parallel)

Terminal 1 -- Claude Code:

claude "Design a NotificationPreference model for our Prisma schema.
Users should be able to set per-channel preferences (email, push, in-app)
for each notification category (marketing, transactional, social).
Include a priority override system. Follow the patterns in our existing
schema at prisma/schema.prisma."

Terminal 2 -- Gemini CLI:

gemini "Read the Firebase Cloud Messaging REST API docs and our existing
notification sender at src/services/notifications/sender.ts. Produce a
summary of: 1) what FCM endpoints we need for topic-based subscriptions,
2) how our current sender would need to change to support per-user
channel preferences, 3) rate limits we need to handle."

Terminal 3 -- Codex:

codex "Generate a database migration that adds a notification_preferences
table with columns for user_id, channel (enum: email, push, in_app),
category (enum: marketing, transactional, social), enabled (boolean),
and priority_override (integer, nullable). Add proper indexes and
foreign key to users table. Follow our migration pattern in
db/migrations/."

All three are working simultaneously. While Claude is reasoning about the data model, Gemini is digesting API documentation, and Codex is producing the migration boilerplate.

Phase 2: Implementation (After Sync)

Once Phase 1 completes and you have reviewed the outputs:

Terminal 1 -- Claude Code:

claude "Implement the NotificationPreferenceService in
src/services/notifications/preferences.ts. It should support
getPreferences(userId), updatePreference(userId, channel, category,
enabled), and resolveDeliveryChannels(userId, notificationType) which
returns which channels a notification should be sent to based on user
preferences and priority overrides. Use the Prisma model we just added."

Terminal 2 -- Gemini CLI:

gemini "Based on the NotificationPreference model in our Prisma schema
and the FCM research, write the OpenAPI spec for these endpoints:
GET /api/notifications/preferences, PUT /api/notifications/preferences,
POST /api/notifications/preferences/bulk-update. Include request/response
schemas, error codes, and example payloads."

Terminal 3 -- Codex:

codex "Write comprehensive unit tests for a NotificationPreferenceService
with methods getPreferences, updatePreference, and resolveDeliveryChannels.
Test edge cases: user with no preferences (should use defaults), conflicting
priority overrides, bulk updates, and invalid channel/category combinations.
Use our Jest setup in tests/ and mock Prisma following the pattern in
tests/services/user.test.ts."

Managing the Chaos: Terminal Organization

The hard part of multi-agent workflows is not the agents themselves. It is keeping track of what is running where. If you have used tmux or iTerm2 split panes for this, you know the friction points:

• Panes get disorganized quickly when you have 3+ agents running
• There is no visual indicator of which agent is in which pane
• You cannot save and restore a multi-agent layout
• Scrollback gets noisy when agents produce long output
• Switching between agent sessions requires remembering which pane is which

You can solve this with careful tmux configuration, custom status bars, and session naming scripts. Or you can use a tool built for exactly this workflow.

How Beam Makes Multi-Agent Workflows Trivial

Beam is a terminal organizer designed around the multi-agent use case. Here is what it does that matters for this workflow:

1-click agent install. Beam auto-detects whether Claude Code, Gemini CLI, and Codex are installed. If they are not, it installs them for you. No npm install -g, no pip install, no PATH configuration. Open Beam, click the agent, and it is ready.

Organized workspaces. Create a workspace for your feature, then open three tabs -- one for each agent. Name them "Claude - Service", "Gemini - Research", "Codex - Tests". The layout persists across sessions, so you can close Beam and reopen it tomorrow with everything exactly where you left it.

Split panes with context. Run all three agents visible simultaneously in split panes within a single window. Each pane shows the agent name and current working directory, so there is no ambiguity about what is running where.

Shared project memory. When you set a working directory for a workspace, every agent session within that workspace starts in the right place. No more cd /path/to/project in every new terminal.

The keyboard shortcuts keep things fast: Cmd+T for a new tab, Cmd+D for a vertical split, Cmd+Shift+D for a horizontal split, and Cmd+1/2/3 to jump between tabs.

Tips for Getting the Most Out of Multi-Agent Workflows

After running this workflow daily, here are the patterns that matter most:

Keep agent prompts focused and scoped. The more specific your task assignment, the better the output. "Write the user service" is worse than "Implement getPreferences and updatePreference methods following our existing service pattern in src/services/user.ts."

Use git branches as isolation boundaries. Each agent can work on its own branch. Merge them together after review. This gives you clean diffs and easy rollback if one agent's output is not up to par.

# Create isolated branches for each agent's work
git checkout -b feat/notifications-model    # for Claude
git checkout -b feat/notifications-tests    # for Codex
git checkout -b feat/notifications-docs     # for Gemini

Review output before letting agents build on each other's work. The sync points are not optional. If Claude produces a flawed data model, you do not want Codex writing 50 tests against it. Take 60 seconds to review before starting Phase 2.

Match the agent to the model's training strengths. This will shift as models improve, but right now: Claude for anything requiring multi-step reasoning, Gemini for anything requiring large input processing, Codex for anything requiring structured pattern following.

Do not over-parallelize. Three agents is the sweet spot for most features. More than that and the coordination overhead starts eating into the time savings. If a feature is complex enough to warrant more agents, it is probably complex enough to break into separate features.

When to Use One Agent vs. Three

Multi-agent workflows are not always the right call. Here is a quick decision framework:

Use a single agent when:

• The task is small and self-contained (a bug fix, a config change)
• All the work touches the same files
• You need deep back-and-forth conversation with context building

Use multiple agents when:

• The feature naturally decomposes into independent subtasks
• Different subtasks require different agent strengths
• You are time-constrained and need parallel throughput
• The work spans distinct parts of the codebase (API, tests, docs, frontend)

Most feature work falls into the second category. If your feature touches both backend and frontend, if it needs tests and documentation, if it requires both research and implementation -- that is a multi-agent problem.